Prettier maps in R and a first step towards interactive maps
Loading some new packages
library(tidyverse)
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library(pander)
library(sf)
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library(terra)
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library(units)
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library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
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library(cartogram)
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library(patchwork)
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library(tmap)
library(viridis)
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library(tigris)
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library(ggspatial)
library(plotly)
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Load the data
#base.dir <- "/Users/mattwilliamson/Google Drive/My Drive/TEACHING/Intro_Spatial_Data_R/Data/session18/"
base.dir <- "/Users/matthewwilliamson/Downloads/session18/"
land.value <- rast(paste0(base.dir,"Regval.tif"))
human.mod <- rast(paste0(base.dir,"hmi.tif"))
mammal.rich <- catalyze(rast(paste0(base.dir,"Mammals_total_richness.tif")))[[2]]
idaho <- states() %>%
filter(., STUSPS == "ID")
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land.value.crop <- terra::crop(land.value, terra::project(vect(idaho), crs(land.value)))
human.mod.crop <- terra::crop(human.mod, terra::project(vect(idaho), crs(human.mod)))
mammal.rich.crop <- terra::crop(mammal.rich, terra::project(vect(idaho), crs(mammal.rich)))
land.value.project <- terra::resample(
terra::project(land.value.crop, crs(human.mod.crop)),
human.mod.crop)
mammal.rich.project <- terra::resample(
terra::project(mammal.rich.crop, crs(human.mod.crop)),
human.mod.crop)
rast.stack <- c(human.mod.crop, land.value.project, mammal.rich.project)
idaho <- idaho %>%
st_transform(., crs = st_crs(rast.stack))
idaho.counties <- counties(state = "ID") %>%
st_transform(., crs = st_crs(rast.stack))
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idaho.pas <- st_read(paste0(base.dir,"westernPAs.shp")) %>%
filter(., Stat_Nm == "ID" ) %>%
st_transform(., crs = st_crs(rast.stack))
## Reading layer `westernPAs' from data source
## `/Users/matthewwilliamson/Downloads/session18/westernPAs.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 8341 features and 34 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -2395532 ymin: 996849.8 xmax: -512593.7 ymax: 3172010
## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
id.census <- tidycensus:: get_acs(geography = "county",
variables = c(medianincome = "B19013_001",
pop = "B01003_001"),
state = c("ID"),
year = 2018,
key = key,
geometry = TRUE) %>%
st_transform(., crs = st_crs(rast.stack)) %>%
dplyr::select(-moe) %>%
spread(variable, estimate)
## Getting data from the 2014-2018 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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#idaho.elevation <- get_elev_raster(locations = idaho, z = 8, expand = 10000)
Building a map with a few layers
bg <- ggmap::get_map(as.vector(st_bbox(st_transform(idaho, 4326))), zoom = 7)
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ggmap(bg) +
geom_sf(data = st_transform(id.census, 4326), mapping = aes(fill = medianincome), inherit.aes = FALSE) +
geom_sf(data=st_transform(idaho.pas, 4326), color="yellow", fill=NA, inherit.aes = FALSE) +
scale_fill_continuous() +
coord_sf(crs = st_crs(4326))
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
ggmap(bg) +
geom_sf(data = st_transform(id.census, 4326), mapping = aes(fill = medianincome), inherit.aes = FALSE) +
geom_sf(data=st_transform(idaho.pas, 4326), color="yellow", fill=NA, inherit.aes = FALSE) +
scale_fill_continuous() +
coord_sf(crs = st_crs(4326)) +
theme(legend.direction = "horizontal", legend.position = "bottom", legend.justification = "center") +
theme_bw()
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
ggmap(bg) +
geom_sf(data = st_transform(id.census, 4326), mapping = aes(fill = medianincome), inherit.aes = FALSE) +
geom_sf(data=st_transform(idaho.pas, 4326), color="yellow", fill=NA, inherit.aes = FALSE) +
scale_fill_continuous() +
coord_sf(crs = st_crs(4326)) +
annotation_scale(location = "tl") +
annotation_north_arrow(location = "br", which_north = "true") +
ggtitle("PAs in Idaho") +
theme(legend.direction = "horizontal", legend.position = "bottom", legend.justification = "center") +
theme_bw()
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## Scale on map varies by more than 10%, scale bar may be inaccurate
main.map <- ggmap(bg) +
geom_sf(data = st_transform(id.census, 4326), mapping = aes(fill = medianincome), inherit.aes = FALSE) +
geom_sf(data=st_transform(idaho.pas, 4326), color="yellow", fill=NA, inherit.aes = FALSE) +
scale_fill_continuous() +
coord_sf(crs = st_crs(4326)) +
annotation_scale(location = "tl") +
annotation_north_arrow(location = "br", which_north = "true") +
ggtitle("PAs in Idaho") +
theme(legend.direction = "horizontal", legend.position = "bottom", legend.justification = "center") +
theme_bw()
not.conus <- c("AK", "HI", "DC", "MP", "GU", "VI", "AS", "PR")
conus <- states() %>%
filter(., !(STUSPS %in% not.conus)) %>%
st_transform(., 4326)
bbox <- st_as_sfc(st_bbox(st_transform(idaho, 4326)))
inset.map <- ggplot(conus)+
geom_sf(fill="lightgray", color="black") +
geom_sf(data = st_as_sfc(st_bbox(st_transform(idaho, 4326))),fill=NA, color = "red") +
theme_blank()
complete <- main.map + inset_element(inset.map, left = 0.6, bottom = 0.6, right = 1, top = 1, align_to = "full")
ggsave("insetmap.png", plot=complete)
p <- ggplot() +
geom_sf(data = st_transform(id.census, 4326), mapping = aes(fill = medianincome), inherit.aes = FALSE) +
geom_sf(data=st_transform(idaho.pas, 4326), color="yellow", fill=NA, inherit.aes = FALSE) +
scale_fill_continuous()
ggplotly(p)